Statistical Models Robustly Predict Membrane Protein Expression in E. Coli

Abstract

Membrane protein production is difficult; their biogenesis does not stop with translation but also requires translocation and integration into a lipid bilayer. These additional steps hamper their heterologous expression which significantly impedes biophysical and structural studies. Detailed and anecdotal evidence in the literature suggests that a variety nucleotide and amino-acid sequence level determinants may potentially support or hinder their biogenesis, e.g. mRNA pausing elements, codon adaptation, transmembrane segment hydrophobicity, “positive inside rule.” In previous work, we demonstrated that a linear, preference-ranking Support Vector Machine can capture heterologous membrane protein expression in E. coli. Here we present work showing statistical models with greatly improved performance across small- and large-scale laboratory experiments (e.g. expression tests that routinely precede structural studies) published in the literature. We also present progress towards a Bayesian model that can be refined as new expression data becomes available on-line and that can be extended across expression systems (e.g. yeast, insect cells), plasmids, and conditions. Furthermore, parameter values from this model will facilitate reliably characterizing the sequence features underpinning membrane protein expression suggesting intriguing areas for further biophysical and computational experiments.